An Efficient Method for High Quality and Cohesive Topical Phrase Mining
An Efficient Method for High Quality and Cohesive Topical Phrase Mining
复制标题
一种高效的高质量、有凝聚力的主题短语挖掘方法
DOI：
10.1109/tkde.2018.2823758
复制
发表时间：
2019
影响因子：
8.9
通讯作者：
Zhang Yanchun
中科院分区：
文献类型：
--
作者：
Li Bing;Yang Xiaochun;Zhou Rui;Wang Bin;Liu Chengfei;Zhang Yanchun
A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency.
